Data visualization is best defined as the tangible representation of information. Traditional processing system based data visualization methodologies consist of plotting an underlying raw data set via a prepackaged set of graphical functions. The extent to which a user may interactively examine the data set in more detail within the environment provided by graphical functions is limited to "zooming in" or "zooming out" on the data. In traditional data visualization methodologies, "zooming" consists of data magnification and demagnification. When zooming in, the observer is moved closer to the data, thereby allowing the observation of the data in greater detail. When zooming out the observer is moved farther away from the data with a resultant loss in observable detail.
In contrast, humans scan large amounts of data searching for patterns that suggest that a particular subset of data should be examined in greater detail. The cognitive process of observing more detail within a contextually interesting, localized data subset involves viewing the data less abstractly, i.e., the act of decreasing the abstraction level of the data is equivalent to observing more information. Conversely, when humans are presented with large amounts of data containing excessive or irrelevant detail, they abstract the data to emphasize essential characteristics, i.e., the act of increasing the abstraction level of the data is equivalent to hiding information. This cognitive process of visually sorting and analyzing data is grounded in two human abilities: (1) the ability to dynamically define local subsets of the data which require abstraction, either upward (information hiding) or downward (information displaying); and (2) the ability to "filter" data when the data is to be abstracted upward. In particular, humans decide the specifics of how information is to be hidden. As is evident from the foregoing, there exists a need in the art for a data visualization system which implements a methodology facilitating both interactive data abstraction and context-dependant information filtering.
A first object of the present invention is to provide data visualization systems and methodologies designed to facilitate interactive, context-sensitive, visual abstraction of data.
A second object of the invention is to provide data visualization systems and methodologies which model the human cognitive process for processing data, i.e., the ability to dynamically sort and analyze large data sets, as well as define local subsets of data which require abstraction, either upward or downward.
A third object of the invention is to provide information filters to both large data sets and respective subsets during upward data abstraction, enabling a user of the invention to determine the specifics of how information is hidden.